By: Calan Smidt

As marketers, we use data to understand how our work is performing. It can help us understand our customers, the ideas that resonate with them and even the channels they want brands to use on their customer journey. That impacts just about every aspect of our campaigns, from creative to budget allocation. But if we’re making decisions on bad data, we may as well guess.

How do you define quality data?

Data quality is defined by many factors. However, what we’re most interested in is whether the data we receive will help us understand our target audiences. 

We receive data from different sources, like social media, form fills and email engagement. With so many channels to gather data from, we keep an eye on the elements that let us know it will be useful.

  • Completeness: Did we get complete information from a form fill? Or did the user smash their keyboard to get through the process faster? The best data has every data point completed.
  • Accurate: Similarly, we want our data to be accurate. That means we need to check for human error, like typos, as well as purposely misleading information. Like if an entry has obviously fake information—like John Smith from Mainstreet, U.S.A.
  • Free of duplicates: We don’t want to inflate our numbers using the same person multiple times. It is important to detect and remove duplicates in our data sets—while also verifying any audience members who might have the same name.

On the other hand, bad quality data is invalid, incorrect or inaccurate data. It can be created accidentally with common misspellings, typos or inconsistent formatting. It can also be created purposefully.

Coming back to the example of the form fill, this type of bad data is created when someone purposely enters inaccurate information. They may not be interested in sharing their data or might find it faster to randomly hit some keys. Either way, they’ve intentionally given inaccurate data.

The impact of bad data: garbage in, garbage out

Customers expect customized experiences. When we don’t take time to verify the quality of the data we use to market to them, we immediately lose the advantage of customization.

For instance, have you ever received a marketing email with your name missing or misspelled? Dear [First Name]doesn’t quite have the same impact as something tailored for you. What about a phone call or text for someone that clearly isn’t you? It probably made you lose respect for the brand reaching out. Maybe even make you feel like they’re untrustworthy.

That’s why we say Garbage In, Garbage Out. Allowing bad data into your system will only offer bad results.

And it’s not just a theory. In 2021, Gartner Research found data quality costs brands an average of $12.9 million each year.

The road to better results: data strategy

If you want good data, there are two steps.

First, clean up your existing data. This is a lot of manual work. After all, there is an entire industry dedicated to cleansing data. But to get the most out of your data, you need to make sure your entries are complete, the formats are consistent and the data you have is accurate.

Next, you’ll need to standardize practices for the future, creating and implementing a data strategy. We recommend:

  • Make quality data an important company priority. Take the time to educate your colleagues on what data you collect, why you collect it, the importance of good data and the impact it can have on your bottom line or your clients’ bottom lines. Everyone can help maintain data quality.
  • Establish data guidelines. Your data collection should be concise, only including information needed under your strategy. Don’t include any fields you won’t use. Eliminate format options to keep data consistent and reduce the need to manually clean it up. For instance, if you want a phone number formatted (515) 555-5555, make sure it can only be inputted in that format.
  • Stay active in the data. You should frequently audit your data, keeping up to date on what you’re collecting and why. First-party data is increasingly valuable as privacy laws change. Make sure you’re aware of what you have, its use and its protection.
  • Be transparent. The people giving you data should understand what you’re collecting and why. Make sure to have an established data and privacy policy. In it, make sure to highlight how you are protecting data and how the data collected will be used. You may need to include additional information for users in states or countries with stricter data regulations.

If you’re looking for guidance on data strategy or need a partner to help maintain your brand’s data quality, reach out to Strategic America. Our data, research and insight experts can help.